The present invention relates to a method and system for determining airborne contaminant concentrations in a building and for determining near-optimal placement of contaminant sensors within buildings.
Because of recent episodes of terrorist attacks, there is much interest in improving ability to sense and locate airborne toxic agents quickly and reliably. Therefore, improved sensing technology is being pursued. However, it is generally not cost effective or practical to have a chemical/biological sensor in every room of a building, as such sensors can be costly. Furthermore, given the significant probability that explosives may be involved that destroy some sensors, or that sensors may be intentionally sabotaged, it is important to have the automated ability to extract the most information from the remaining set of sensors. Accordingly, there is a great need for computer-based methods that can rapidly process whatever sensor data are being generated with the objective to rapidly determine agent concentrations throughout a building. This information can then be used to determine the source (or sources) of contamination, plan evacuations, plan mitigation and decontamination efforts, and provide initial conditions for detailed predictive modeling. Thus it is imperative that the calculations can be performed quickly with real-world data that are often incomplete and noisy.
Although smoke detectors are now so inexpensive that they are routinely mounted in many locations throughout commercial and government buildings, sensors for chemical and/or biological agents can be orders of magnitude more expensive, and therefore the cost of liberally distributing, maintaining, and monitoring many such sensors throughout a facility can be prohibitive. Thus there is a great incentive to optimally locate smaller numbers of sensors, while sacrificing as little accuracy as possible of the sensing capability of a liberally distributed system. Accordingly, an automated method for optimally locating a small number of expensive sensors is also needed.
The present invention provides both capabilities because it provides the ability to combine the airflow characteristics of a structure with limited sensor data into a form that can be solved using a least-squares algorithm.
The present invention relates to a method and system for inferring airborne contaminant concentrations in rooms without contaminant sensors, based on data collected by contaminant sensors in other rooms of a building, using known airflow interconnectivity data. The method solves a least squares problem that minimizes the difference between measured and predicted contaminant sensor concentrations with respect to an unknown contaminant release time. Solutions are constrained to providing non-negative initial contaminant concentrations in all rooms. The method can be used to identify a near-optimal distribution of sensors within the building, when the number of available sensors is less than the total number of rooms. This is achieved by having a system-sensor matrix that is non-singular, and by selecting that distribution which yields the lowest condition number of all the distributions considered. The method can predict one or more contaminant initial release points from the collected data.
The method can predict one or more contaminant initial release points from the received and the inferred concentration data. The received and the inferred concentration data can then be reported to a system for determining and communicating preferred escape routes for personnel in the building, and for determining the most efficient decontamination scheme.
The present invention is also of an apparatus for and method of placing a number of contaminant sensors that is less than the total number of rooms of a building, comprising: determining a distribution having a system-sensor matrix that is non-singular and having the lowest condition number of all distributions considered; and placing the available contaminant sensors in the determined distribution, wherein the contaminant sensor placement distribution is near-optimal with respect to inferring contaminant concentration data for rooms in the building not having a contaminant sensor from flow interconnectivity information for the building and contaminant concentration data from the contaminant sensors.